In modern external beam radiation therapy, the cone-beam computed tomography (CBCT) system is the most commonly used imaging modality for patient position verification during the treatment process [1-5]. By matching three dimensional (3D) CBCT volumetric images to the planned CT, the patient's treatment position can be determined accurately, and subsequently, setup parameters can be adjusted to deliver the intended treatment [6-8]. Moreover, CBCT plays a crucial role in adaptive radiotherapy by providing information on patient position and anatomical variation [1, 2, 9-12].
In addition to 3D volumetric imaging, the utilization of respiratory correlated four-dimensional (4D) imaging schemes, such as 4DCBCT, have also recently become available [8, 13-16]. 4DCBCT images represent a sequence of images, formed at successive times, which therefore can illustrate moving portions of an object being imaged. 4DCBCT allows clinicians to visualize and verify tumor motion as well as organ motion prior to the radiotherapy treatment [17, 18]. Compared to 3DCBCT, 4DCBCT can not only provide tumor motion trajectories during the respiratory process, but it can also significantly reduce motion artifacts on the images. Thus, the 4DCBCT technique can ultimately enhance the target localization accuracy [16-18], especially for high precision treatments such as lung stereotactic body radiation therapy (SBRT) [19-21].
In 4DCBCT, all acquired X-ray projections are first grouped into different respiratory phase bins according to breathing signal amplitude tagged on the images. The CBCT image for each breathing phase is then reconstructed independently. The reconstructed 3DCBCT images for each phase are finally consolidated into a 4DCBCT image set.
Unfortunately, such a scheme usually cannot provide a sufficient number of projections for the image reconstruction of each individual phase, which results in severe streaking artifacts, especially when the FDK reconstruction algorithm [22] is applied. One way to solve this problem is to increase the sample number of X-ray projections to a sufficient level. However, such an approach not only inevitably escalates the scanning dose to the patient by multiple folds [23-25], but it can also increase the total scan time when the tube firing rate and/or image panel update rate becomes a bottle neck for the imaging system under a high projection number request [25]. In general, compared to 3D CBCT, the image quality of commercially available 4DCBCT is severely impaired due to the insufficient amount of projection data available for each phase without increased sampling.
Some investigators [26-32] have developed other reconstruction techniques, which can be divided into two categories: 1) post-processing based [26-28] and 2) iterative reconstruction (IR) based [29-32]. Techniques in the first category enhance the 4DCBCT images by deforming a priori-reconstructed 4DCBCT images for all phases into one, and subsequently, superimposing them together. The performance of these approaches, however, largely depends on the quality of a priori-reconstructed 4DCBCT, as well as on the accuracy of the deformable image registration algorithm. The algorithm can become impractical, as the a priori-4DCBCT image provides insufficient anatomical information due to the unsatisfactory quality. The latter techniques originate from compressed sensing theory [33]. Casting the 4DCBCT reconstruction problem into a convex optimization problem with regulation constraints, these techniques typically solve the 4DCBCT image using an iterative process. During each iteration, the optimization engine is driven by evaluating back-projected results of the discrepancy between forward projections of the reconstructed 4DCBCT and the original projections of the corresponding phase. Various forms of prior knowledge, such as 3D free breathing CBCT (FB-CBCT), planning CT, and motion model [34-42], have also been incorporated into the models to improve the performance of the algorithms. Using IR-based reconstruction techniques, a better quality 4DCBCT image can be achievable without the need for increasing the projection number for the current 4DCBCT scan.
Recently, the inventors proposed an efficient IR-based 4DCBCT reconstruction framework called the motion-map constrained image reconstruction (MCIR) algorithm [43]. MCIR utilizes motion masks subjected to respiratory motion in the reconstruction space to separate moving and static portions of the volumes. The 4DCBCT images can be reconstructed by updating volumetric components that are defined by motion components using retrospectively sorted projection data, while keeping stationary components with a priori-reconstructed FB-CBCT images. Using only 3DCBCT projections, the MCIR algorithm is able to reconstruct 4DCBCT images with modest improved quality, in terms of noise and the aliasing artifact, compared with other IR-based algorithms.